Generalized gene co-expression analysis via subspace clustering using low-rank representation

BACKGROUND: Gene Co-expression Network Analysis (GCNA) helps identify gene modules with potential biological functions and has become a popular method in bioinformatics and biomedical research. However, most current GCNA algorithms use correlation to build gene co-expression networks and identify modules with highly correlated genes. There is a need to look beyond correlation and identify gene modules using other similarity measures for finding novel biologically meaningful modules. RESULTS: We propose a new generalized gene co-expression analysis algorithm via subspace clustering that can identify biologically meaningful gene co-expression modules with genes that are not all highly correlated. We use low-rank representation to construct gene co-expression networks and local maximal quasi-clique merger to identify gene co-expression modules. We applied our method on three large microarray datasets and a single-cell RNA sequencing dataset. We demonstrate that our method can identify gene modules with different biological functions than current GCNA methods and find gene modules with prognostic values. CONCLUSIONS: The presented method takes advantage of subspace clustering to generate gene co-expression networks rather than using correlation as the similarity measure between genes. Our generalized GCNA method can provide new insights from gene expression datasets and serve as a complement to current GCNA algorithms

Prediction of nonlinear interface dynamics in the unidirectional freezing of particle suspensions with rigid compacted layer

Water freezing in particle suspensions widely exists in nature. As a typical physical system of free boundary problem, the spatiotemporal evolution of the solid/liquid interface not only origins from phase transformation but also from permeation flow in front of ice. Physical models have been proposed in previous efforts to describe the interface dynamic behaviors in unidirectional freezing of particle suspensions. However, there are several physical parameters difficult to be determined in previous investigations dedicated to describing the spatiotemporal evolution in unidirectional freezing of particle suspensions. Here, based on the fundamental momentum theorem, we propose a consistent theoretical framework to address the unidirectional freezing process in the particle suspensions coupled with the effect of water permeation. An interface undercooling-dependent pushing force exerted on the compacted layer with a specific formula is derived based on the surface tension. Then a dynamic compacted layer is considered and analyzed. Numerical solutions of the nonlinear models reveal the dependence of system dynamics on some typical physical parameters, particle radius, initial particle concentration in the suspensions, freezing velocity and so on. The system dynamics are characterized by interface velocity, interface undercooling and interface recoil as functions of time. The models allow us to reconsider the formation mechanism of ice spears in freezing of particle suspensions in a simpler but novel way, with potential implications for both understanding and controlling not only ice formation in porous media but also crystallization processes in other complex systems

TENSILE: A Tensor granularity dynamic GPU memory scheduling method towards multiple dynamic workloads system

Recently, deep learning has been an area of intense research. However, as a kind of computing-intensive task, deep learning highly relies on the scale of GPU memory, which is usually prohibitive and scarce. Although there are some extensive works have been proposed for dynamic GPU memory management, they are hard to be applied to systems with multiple dynamic workloads, such as in-database machine learning systems. In this paper, we demonstrated TENSILE, a method of managing GPU memory in tensor granularity to reduce the GPU memory peak, considering the multiple dynamic workloads. TENSILE tackled the cold-starting and across-iteration scheduling problem existing in previous works. We implement TENSILE on a deep learning framework built by ourselves and evaluated its performance. The experiment results show that TENSILE can save more GPU memory with less extra time overhead than prior works in both single and multiple dynamic workloads scenarios